Date of download: 7/5/2016 Copyright © 2016 SPIE. All rights reserved. Workflow of our statistical framework for texture feature equivalence assessment. IS, digital mammography image acquired from system S; IS,norm, IS after image intensity normalization; FS,T, texture feature image for system S, texture feature T. Figure Legend: From: Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices J. Med. Imag. 2015;2(2): doi: /1.JMI

Date of download: 7/5/2016 Copyright © 2016 SPIE. All rights reserved. Raw digital mammogram of the Rachel phantom acquired on GE 2000D: (a) the original raw phantom image, (b) exclusion of the breast phantom bounding box, shown after the inverse-log transformation for visualization, and (c) the binary mask of the breast region. Figure Legend: From: Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices J. Med. Imag. 2015;2(2): doi: /1.JMI

Date of download: 7/5/2016 Copyright © 2016 SPIE. All rights reserved. (a) The lattice scheme for texture image generation and (b) a gray-level histogram (STD) image. Figure Legend: From: Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices J. Med. Imag. 2015;2(2): doi: /1.JMI

Date of download: 7/5/2016 Copyright © 2016 SPIE. All rights reserved. Example feature maps [(a), (c), and (e)] and feature-value histograms [(b), (d), and (f)] of the co-occurrence inertia (INT) feature computed on the GE 2000D [(a) and (b)], GE DS [(c) and (d)], and Hologic Selenia Dimensions [(e) and (f)] digital mammography systems using a window size of 127 pixels2 and offset of l=7 pixels. Figure Legend: From: Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices J. Med. Imag. 2015;2(2): doi: /1.JMI

Date of download: 7/5/2016 Copyright © 2016 SPIE. All rights reserved. Example feature maps [(a), (c), and (e)] and feature-value histograms [(b), (d), and (f)] of the co-occurrence inverse difference moment feature computed on the GE 2000D [(a) and (b)], GE DS [(c) and (d)], and Hologic Selenia Dimensions [(e) and (f)] digital mammography systems using a window size of 63 pixels2 and offset of l=1 pixel. Figure Legend: From: Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices J. Med. Imag. 2015;2(2): doi: /1.JMI

Date of download: 7/5/2016 Copyright © 2016 SPIE. All rights reserved. Illustration of the effect of the window size (W) on the texture feature images. Representative examples shown in each row, from left to right, for GE 2000D raw images (after -log transformation) and corresponding texture feature images with increasing window sizes of 15, 31, 63, and 127 pixels2 for texture features (a) STD (gray-level histogram); (b) GLN (run length); (c) EEI (structural). The texture feature images are generated from z-score normalized raw images acquired on the GE 2000D system. Figure Legend: From: Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices J. Med. Imag. 2015;2(2): doi: /1.JMI

Date of download: 7/5/2016 Copyright © 2016 SPIE. All rights reserved. Co-occurrence feature energy with varying window size and offset length. Each row indicates one window size (31, 63, 127 pixels2) and each column indicates one offset length (1, 5, 9 pixels). Figure Legend: From: Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices J. Med. Imag. 2015;2(2): doi: /1.JMI